PyTorch Intro Tutorial ๐ฅ Code and Train a basic Neural Network (with free GPU!)
Learn the PyTorch TOP 10 Basic Concepts and Build a Neural Network from scratch!
Kaggle Notebook: https://www.kaggle.com/code/edomingo/intro-to-pytorch-in-10-code-cells
Blog: https://medium.com/@enricdomingo/intro-to-pytorch-create-and-train-a-basic-neural-network-with-free-gpu-cd9cfc673cbd
This programming tutorial video is a quick introduction to PyTorch! A Deep Learning framework, released by Facebook (Meta) 7 years ago, that is currently behind all OpenAI's models (GTP-3, GPT-4, Dalle, etc.), Tesla's Autopilot, Stable Diffusion, and many other popular (and not so popular but industry wide) and important AI systems. In this PyTorch tutorial we will learn the basic concepts step by step, from how to install torch (Pytorch pypi library) to how to build and train a simple Neural Network model, and to run PyTorch in an NVIDIA GPU. This is a fundamental skill for any Data Scientist and ML Engineer.
With PyTorch, we can create, train and serve Machine Learning and Deep Learning models flexibly, for many different use cases: from Language Models, Computer Vision predicting on image, detect cancer on DNA, classify audio files or even predict the next week's weather with Time Series data, to name some random examples...
SECTIONS:
00:00 - Intro
2:10 - Install and Import torch (PyTorch)
3:40 - Tensor Operations
5:01 - Automatic Differentiation
7:03 - Datasets and Dataloaders
14:03 - Neural Networks
18:30 - Loss Function and Optimizer
19:42 - Training Loop
21:47 - GPU Support
25:25 - Saving, Loading, and using Models
27:32 - Conclusion
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Kaggle: https://www.kaggle.com/edomingo
GitHub: https://github.com/enricd
Twitter: https://twitter.com/mad_enrico
Linkedin: https://www.linkedin.com/in/e-domingo/
Medium: https://medium.com/@enricdomingo
Web: https://enricdomingo.com
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Song: Del - Tropical Love (Vlog No Copyright Music)
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Chapters (11)
Intro
2:10
Install and Import torch (PyTorch)
3:40
Tensor Operations
5:01
Automatic Differentiation
7:03
Datasets and Dataloaders
14:03
Neural Networks
18:30
Loss Function and Optimizer
19:42
Training Loop
21:47
GPU Support
25:25
Saving, Loading, and using Models
27:32
Conclusion
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Tutor Explanation
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